Institutions per country
universities_per_country <- works %>%
distinct(country_code, institution_id) %>%
count(country_code, name = "n_universities") %>%
arrange(desc(n_universities)) %>%
collect() %>%
add_country()
universities_per_country %>%
select(country, n_universities) %>%
knitr::kable()
| United States |
6808 |
| India |
2061 |
| China |
1985 |
| United Kingdom |
1610 |
| Japan |
1444 |
| Germany |
1033 |
| France |
1024 |
| Brazil |
807 |
| Canada |
746 |
| Russian Federation |
665 |
| Spain |
642 |
| Italy |
582 |
| Australia |
538 |
| Korea, Rep. |
478 |
| NA |
433 |
| Indonesia |
407 |
| Poland |
333 |
| Netherlands |
329 |
| Switzerland |
293 |
| Taiwan, China |
264 |
| Pakistan |
240 |
| Turkiye |
240 |
| Mexico |
236 |
| Iran, Islamic Rep. |
234 |
| Norway |
219 |
| Colombia |
205 |
| Ukraine |
199 |
| Czech Republic |
195 |
| Sweden |
186 |
| Portugal |
181 |
| Austria |
177 |
| Nigeria |
171 |
| South Africa |
168 |
| Belgium |
158 |
| Thailand |
156 |
| Argentina |
151 |
| Greece |
145 |
| Finland |
144 |
| Denmark |
142 |
| Ireland |
138 |
| Bangladesh |
132 |
| Philippines |
122 |
| Malaysia |
121 |
| Israel |
120 |
| Hungary |
112 |
| New Zealand |
108 |
| Vietnam |
103 |
| Chile |
97 |
| Singapore |
97 |
| Egypt, Arab Rep. |
96 |
| Saudi Arabia |
91 |
| Slovak Republic |
89 |
| Kenya |
86 |
| Romania |
80 |
| Bulgaria |
80 |
| Peru |
78 |
| Slovenia |
76 |
| Ecuador |
65 |
| Uganda |
64 |
| United Arab Emirates |
60 |
| Serbia |
60 |
| Croatia |
59 |
| Ghana |
54 |
| Ethiopia |
54 |
| Iraq |
52 |
| Kazakhstan |
51 |
| Tanzania |
48 |
| Algeria |
47 |
| Nepal |
46 |
| Belarus |
40 |
| Venezuela, RB |
39 |
| Sri Lanka |
38 |
| Cuba |
38 |
| Latvia |
38 |
| Lithuania |
36 |
| Tunisia |
33 |
| Estonia |
33 |
| Jordan |
32 |
| Morocco |
28 |
| Zimbabwe |
28 |
| Cameroon |
27 |
| Sudan |
27 |
| Uzbekistan |
27 |
| Uruguay |
27 |
| Lebanon |
26 |
| Cyprus |
26 |
| Costa Rica |
25 |
| Armenia |
23 |
| Luxembourg |
23 |
| Georgia |
22 |
| Oman |
21 |
| Albania |
20 |
| Cambodia |
20 |
| Bolivia |
20 |
| Azerbaijan |
19 |
| Syrian Arab Republic |
19 |
| Myanmar |
19 |
| Kuwait |
19 |
| Bosnia and Herzegovina |
19 |
| Qatar |
19 |
| Iceland |
19 |
| Congo, Dem. Rep. |
18 |
| Paraguay |
17 |
| West Bank and Gaza |
17 |
| Zambia |
16 |
| Malawi |
15 |
| Dominican Republic |
15 |
| Panama |
15 |
| Yemen, Rep. |
14 |
| Mozambique |
14 |
| Mongolia |
14 |
| Moldova |
14 |
| Kyrgyz Republic |
14 |
| Senegal |
14 |
| Bahrain |
13 |
| Burkina Faso |
13 |
| Guatemala |
13 |
| North Macedonia |
13 |
| Rwanda |
12 |
| Hong Kong SAR, China |
11 |
| Cote d'Ivoire |
10 |
| Botswana |
10 |
| Libya |
10 |
| Afghanistan |
10 |
| Nicaragua |
9 |
| Tajikistan |
9 |
| El Salvador |
9 |
| Trinidad and Tobago |
8 |
| Somalia |
8 |
| Madagascar |
7 |
| Angola |
7 |
| Namibia |
7 |
| Papua New Guinea |
7 |
| Malta |
6 |
| Mauritius |
6 |
| Mali |
6 |
| Niger |
6 |
| Lao PDR |
5 |
| Gambia, The |
5 |
| Kosovo |
5 |
| Jamaica |
5 |
| Honduras |
5 |
| Benin |
5 |
| Montenegro |
5 |
| Fiji |
5 |
| Brunei Darussalam |
4 |
| Togo |
4 |
| Korea, Dem. People's Rep. |
4 |
| Gabon |
4 |
| South Sudan |
4 |
| Curacao |
4 |
| Liechtenstein |
3 |
| Bermuda |
3 |
| Greenland |
3 |
| Belize |
3 |
| Sierra Leone |
3 |
| Monaco |
3 |
| Liberia |
3 |
| Maldives |
3 |
| St. Kitts and Nevis |
3 |
| Bahamas, The |
3 |
| Mauritania |
3 |
| Guinea-Bissau |
3 |
| Turkmenistan |
3 |
| French Polynesia |
3 |
| Puerto Rico |
3 |
| NA |
2 |
| Congo, Rep. |
2 |
| Guyana |
2 |
| Seychelles |
2 |
| Burundi |
2 |
| St. Lucia |
2 |
| Macao SAR, China |
2 |
| New Caledonia |
2 |
| Eswatini |
2 |
| Haiti |
2 |
| Bhutan |
2 |
| Sint Maarten (Dutch part) |
2 |
| Antigua and Barbuda |
2 |
| Suriname |
2 |
| NA |
2 |
| Chad |
2 |
| Micronesia, Fed. Sts. |
1 |
| Marshall Islands |
1 |
| NA |
1 |
| Andorra |
1 |
| British Virgin Islands |
1 |
| Timor-Leste |
1 |
| Grenada |
1 |
| NA |
1 |
| NA |
1 |
| Cayman Islands |
1 |
| Sao Tome and Principe |
1 |
| Faroe Islands |
1 |
| Gibraltar |
1 |
| Cabo Verde |
1 |
| NA |
1 |
| Barbados |
1 |
| Lesotho |
1 |
| St. Vincent and the Grenadines |
1 |
| Isle of Man |
1 |
| Central African Republic |
1 |
| Palau |
1 |
| Eritrea |
1 |
| NA |
1 |
| Guinea |
1 |
| Aruba |
1 |
| NA |
1 |
| NA |
1 |
# papers per country
papers_per_country <- works %>%
distinct(country_code, id, work_frac, author_position, institution_id) %>%
group_by(country_code) %>%
summarise(sum_fractional_works = sum(work_frac) %>% round(digits = 1)) %>%
arrange(desc(sum_fractional_works)) %>%
collect() %>%
add_country()
papers_per_country %>%
select(country, country_code, sum_fractional_works) %>%
knitr::kable()
| United States |
US |
203117.5 |
| Brazil |
BR |
133621.1 |
| China |
CN |
125471.6 |
| United Kingdom |
GB |
52770.8 |
| Spain |
ES |
42338.3 |
| Germany |
DE |
41978.4 |
| India |
IN |
35913.0 |
| Canada |
CA |
33396.2 |
| Japan |
JP |
32637.3 |
| Australia |
AU |
26479.1 |
| Korea, Rep. |
KR |
26068.3 |
| Italy |
IT |
24002.1 |
| Poland |
PL |
22138.4 |
| France |
FR |
21485.3 |
| Indonesia |
ID |
20531.2 |
| South Africa |
ZA |
14615.0 |
| Iran, Islamic Rep. |
IR |
14492.1 |
| Netherlands |
NL |
14466.7 |
| Mexico |
MX |
14207.0 |
| Taiwan, China |
TW |
13904.9 |
| Russian Federation |
RU |
13024.2 |
| Colombia |
CO |
12862.2 |
| Sweden |
SE |
12675.9 |
| Turkiye |
TR |
11371.6 |
| Switzerland |
CH |
10476.5 |
| Malaysia |
MY |
9665.3 |
| Argentina |
AR |
9441.5 |
| Portugal |
PT |
8995.1 |
| Belgium |
BE |
7863.8 |
| Chile |
CL |
7724.4 |
| Norway |
NO |
7441.1 |
| Denmark |
DK |
7122.5 |
| Saudi Arabia |
SA |
6564.3 |
| Czech Republic |
CZ |
5953.0 |
| Israel |
IL |
5819.2 |
| Austria |
AT |
5562.1 |
| Pakistan |
PK |
5545.0 |
| Egypt, Arab Rep. |
EG |
5508.9 |
| Finland |
FI |
4861.7 |
| Ukraine |
UA |
4487.1 |
| Thailand |
TH |
4401.0 |
| Nigeria |
NG |
4234.0 |
| Singapore |
SG |
4192.0 |
| New Zealand |
NZ |
3808.6 |
| Greece |
GR |
3521.3 |
| Ireland |
IE |
3369.2 |
| NA |
NA |
3025.4 |
| Hungary |
HU |
2867.2 |
| Serbia |
RS |
2729.7 |
| Ethiopia |
ET |
2692.8 |
| Croatia |
HR |
2683.6 |
| Romania |
RO |
2662.8 |
| Peru |
PE |
2632.4 |
| Slovenia |
SI |
2283.8 |
| Slovak Republic |
SK |
2183.5 |
| Bangladesh |
BD |
1954.4 |
| Costa Rica |
CR |
1879.4 |
| Ecuador |
EC |
1547.1 |
| Bulgaria |
BG |
1389.9 |
| Lithuania |
LT |
1386.7 |
| Ghana |
GH |
1339.3 |
| Iraq |
IQ |
1281.8 |
| Bahrain |
BH |
1078.6 |
| Morocco |
MA |
1070.8 |
| Nepal |
NP |
1056.8 |
| Uruguay |
UY |
1006.5 |
| United Arab Emirates |
AE |
994.7 |
| Jordan |
JO |
992.7 |
| Vietnam |
VN |
986.5 |
| Estonia |
EE |
985.8 |
| Sri Lanka |
LK |
858.5 |
| Kenya |
KE |
854.9 |
| Philippines |
PH |
775.4 |
| Qatar |
QA |
761.7 |
| Lebanon |
LB |
738.6 |
| Venezuela, RB |
VE |
724.4 |
| Tunisia |
TN |
720.5 |
| Uganda |
UG |
604.0 |
| Mozambique |
MZ |
592.3 |
| Cyprus |
CY |
582.4 |
| Tanzania |
TZ |
549.3 |
| Oman |
OM |
524.7 |
| Kuwait |
KW |
510.6 |
| Cameroon |
CM |
490.3 |
| Latvia |
LV |
441.9 |
| Bosnia and Herzegovina |
BA |
425.8 |
| Cuba |
CU |
403.0 |
| Algeria |
DZ |
359.4 |
| Iceland |
IS |
329.0 |
| Zimbabwe |
ZW |
326.9 |
| El Salvador |
SV |
316.0 |
| Luxembourg |
LU |
303.4 |
| Armenia |
AM |
289.9 |
| Jamaica |
JM |
288.5 |
| Kazakhstan |
KZ |
277.9 |
| Hong Kong SAR, China |
HK |
277.5 |
| West Bank and Gaza |
PS |
276.2 |
| Benin |
BJ |
267.3 |
| Belarus |
BY |
255.3 |
| Sudan |
SD |
254.0 |
| Antigua and Barbuda |
AG |
238.8 |
| Puerto Rico |
PR |
235.8 |
| Paraguay |
PY |
209.9 |
| Cambodia |
KH |
209.4 |
| Syrian Arab Republic |
SY |
197.8 |
| Malta |
MT |
178.1 |
| Botswana |
BW |
177.6 |
| Panama |
PA |
168.5 |
| Zambia |
ZM |
166.0 |
| Bolivia |
BO |
159.1 |
| Uzbekistan |
UZ |
153.0 |
| Montenegro |
ME |
145.9 |
| Lao PDR |
LA |
144.4 |
| Azerbaijan |
AZ |
142.7 |
| Albania |
AL |
141.6 |
| Kosovo |
XK |
141.5 |
| North Macedonia |
MK |
141.4 |
| Guatemala |
GT |
127.2 |
| Malawi |
MW |
124.6 |
| Angola |
AO |
123.5 |
| Yemen, Rep. |
YE |
122.2 |
| Nicaragua |
NI |
118.7 |
| Georgia |
GE |
114.2 |
| Senegal |
SN |
98.7 |
| Mongolia |
MN |
82.4 |
| Brunei Darussalam |
BN |
80.1 |
| Libya |
LY |
74.2 |
| Sao Tome and Principe |
ST |
73.2 |
| Bahamas, The |
BS |
70.7 |
| NA |
RE |
70.6 |
| Namibia |
NA |
68.1 |
| Rwanda |
RW |
65.0 |
| Myanmar |
MM |
63.3 |
| Burkina Faso |
BF |
62.0 |
| Moldova |
MD |
58.6 |
| St. Kitts and Nevis |
KN |
58.2 |
| Dominican Republic |
DO |
57.1 |
| Mauritius |
MU |
56.6 |
| Cote d'Ivoire |
CI |
53.3 |
| Fiji |
FJ |
51.1 |
| Grenada |
GD |
46.4 |
| Mali |
ML |
45.8 |
| NA |
GP |
45.2 |
| Congo, Dem. Rep. |
CD |
44.5 |
| Barbados |
BB |
41.1 |
| Honduras |
HN |
39.6 |
| Tajikistan |
TJ |
32.6 |
| Kyrgyz Republic |
KG |
31.4 |
| Togo |
TG |
31.3 |
| Papua New Guinea |
PG |
28.9 |
| Gabon |
GA |
25.7 |
| Lesotho |
LS |
25.3 |
| Trinidad and Tobago |
TT |
23.6 |
| NA |
SJ |
22.6 |
| Niger |
NE |
21.5 |
| French Polynesia |
PF |
20.3 |
| Afghanistan |
AF |
18.1 |
| Bhutan |
BT |
16.5 |
| Korea, Dem. People's Rep. |
KP |
16.0 |
| Eswatini |
SZ |
15.9 |
| British Virgin Islands |
VG |
15.1 |
| Cabo Verde |
CV |
15.0 |
| Sierra Leone |
SL |
14.7 |
| Bermuda |
BM |
14.5 |
| Congo, Rep. |
CG |
13.6 |
| Gambia, The |
GM |
12.2 |
| Madagascar |
MG |
12.1 |
| Guinea-Bissau |
GW |
12.0 |
| Guinea |
GN |
10.8 |
| Suriname |
SR |
10.3 |
| Curacao |
CW |
9.8 |
| Faroe Islands |
FO |
9.6 |
| Greenland |
GL |
9.1 |
| New Caledonia |
NC |
7.9 |
| Guyana |
GY |
7.9 |
| Burundi |
BI |
6.1 |
| Liechtenstein |
LI |
5.5 |
| NA |
GF |
4.2 |
| St. Vincent and the Grenadines |
VC |
4.2 |
| Marshall Islands |
MH |
4.1 |
| Sint Maarten (Dutch part) |
SX |
3.3 |
| Somalia |
SO |
3.2 |
| Monaco |
MC |
3.1 |
| South Sudan |
SS |
2.9 |
| Turkmenistan |
TM |
2.5 |
| Mauritania |
MR |
2.4 |
| St. Lucia |
LC |
2.3 |
| Chad |
TD |
2.3 |
| NA |
JE |
2.0 |
| Maldives |
MV |
2.0 |
| Liberia |
LR |
1.9 |
| Macao SAR, China |
MO |
1.5 |
| Belize |
BZ |
1.5 |
| Micronesia, Fed. Sts. |
FM |
1.0 |
| Cayman Islands |
KY |
1.0 |
| Timor-Leste |
TL |
0.8 |
| Andorra |
AD |
0.8 |
| Haiti |
HT |
0.8 |
| Aruba |
AW |
0.7 |
| Palau |
PW |
0.6 |
| Gibraltar |
GI |
0.6 |
| Seychelles |
SC |
0.5 |
| Eritrea |
ER |
0.5 |
| NA |
MQ |
0.5 |
| Central African Republic |
CF |
0.4 |
| NA |
MS |
0.3 |
| Isle of Man |
IM |
0.3 |
| NA |
VA |
0.1 |
| NA |
AX |
0.1 |
# average apc
average_apc <- works %>%
# first get rid of duplicates from concepts
distinct(country_code, id, work_frac, author_position, institution_id,
APC_in_dollar) %>%
group_by(country_code) %>%
# compute the average APC using fractional authorships as weights
mutate(sum_frac = sum(work_frac)) %>%
group_by(country_code, sum_frac) %>%
summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>%
collect() %>%
add_country()
## `summarise()` has grouped output by 'country_code'. You can override using the
## `.groups` argument.
# average APC over time
average_apc_time <- works %>%
# first get rid of duplicates from concepts
distinct(country_code, id, work_frac, author_position, institution_id,
APC_in_dollar, publication_year) %>%
group_by(country_code, publication_year) %>%
# compute the average APC using fractional authorships as weights
mutate(sum_frac = sum(work_frac)) %>%
group_by(country_code, sum_frac, publication_year) %>%
summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>%
collect()
## `summarise()` has grouped output by 'country_code', 'sum_frac'. You can override
## using the `.groups` argument.
average_apc_time %>%
left_join(wdi, by = c("country_code" = "iso2c")) %>%
ggplot(aes(publication_year, mean_apc)) +
geom_line(aes(group = country), alpha = .3) +
geom_smooth(se = FALSE, colour = "#007FA8") +
facet_wrap(vars(region)) +
scale_x_continuous(breaks = scales::pretty_breaks(6)) +
coord_cartesian(ylim = c(0, 3000)) +
labs(x = NULL, y = "Mean APC")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

All three joined
all_three_descriptives <- universities_per_country %>%
left_join(papers_per_country, by = c("country", "country_code")) %>%
left_join(average_apc, by = c("country", "country_code")) %>%
# remove missing values
# these arise since the wdi data on country codes does not have a mapping for
# all codes that are present in the data. in most cases, these are small
# countries, and in the case of a high university count (433) simply all
# institutions that were not assigned a country code at all.
drop_na()
all_three_descriptives %>%
arrange(desc(n_universities), desc(sum_fractional_works)) %>%
mutate(mean_apc = round(mean_apc, digits = 1),
sum_fractional_works = scales::comma(sum_fractional_works)) %>%
select(Country = country, `n universities` = n_universities,
`n fractional publications` = sum_fractional_works,
`Mean APC` = mean_apc) %>%
knitr::kable()
| United States |
6808 |
203,117.500 |
1834.2 |
| India |
2061 |
35,913.000 |
758.8 |
| China |
1985 |
125,471.600 |
1823.6 |
| United Kingdom |
1610 |
52,770.800 |
1792.4 |
| Japan |
1444 |
32,637.300 |
1778.9 |
| Germany |
1033 |
41,978.400 |
1838.9 |
| France |
1024 |
21,485.300 |
1596.0 |
| Brazil |
807 |
133,621.100 |
232.4 |
| Canada |
746 |
33,396.200 |
1666.3 |
| Russian Federation |
665 |
13,024.200 |
474.8 |
| Spain |
642 |
42,338.300 |
804.4 |
| Italy |
582 |
24,002.100 |
1673.6 |
| Australia |
538 |
26,479.100 |
1804.4 |
| Korea, Rep. |
478 |
26,068.300 |
1676.7 |
| Indonesia |
407 |
20,531.200 |
153.7 |
| Poland |
333 |
22,138.400 |
779.0 |
| Netherlands |
329 |
14,466.700 |
1851.9 |
| Switzerland |
293 |
10,476.500 |
1912.1 |
| Taiwan, China |
264 |
13,904.900 |
1800.1 |
| Turkiye |
240 |
11,371.600 |
781.4 |
| Pakistan |
240 |
5,545.000 |
1083.2 |
| Mexico |
236 |
14,207.000 |
582.3 |
| Iran, Islamic Rep. |
234 |
14,492.100 |
721.1 |
| Norway |
219 |
7,441.100 |
1597.1 |
| Colombia |
205 |
12,862.200 |
198.6 |
| Ukraine |
199 |
4,487.100 |
290.1 |
| Czech Republic |
195 |
5,953.000 |
1054.1 |
| Sweden |
186 |
12,675.900 |
1786.4 |
| Portugal |
181 |
8,995.100 |
784.8 |
| Austria |
177 |
5,562.100 |
1677.0 |
| Nigeria |
171 |
4,234.000 |
824.5 |
| South Africa |
168 |
14,615.000 |
978.7 |
| Belgium |
158 |
7,863.800 |
1676.6 |
| Thailand |
156 |
4,401.000 |
1397.1 |
| Argentina |
151 |
9,441.500 |
398.2 |
| Greece |
145 |
3,521.300 |
1446.4 |
| Finland |
144 |
4,861.700 |
1598.4 |
| Denmark |
142 |
7,122.500 |
1778.9 |
| Ireland |
138 |
3,369.200 |
1738.8 |
| Bangladesh |
132 |
1,954.400 |
766.2 |
| Philippines |
122 |
775.400 |
931.1 |
| Malaysia |
121 |
9,665.300 |
1053.4 |
| Israel |
120 |
5,819.200 |
1917.2 |
| Hungary |
112 |
2,867.200 |
1250.0 |
| New Zealand |
108 |
3,808.600 |
1579.4 |
| Vietnam |
103 |
986.500 |
1254.8 |
| Chile |
97 |
7,724.400 |
521.6 |
| Singapore |
97 |
4,192.000 |
1874.8 |
| Egypt, Arab Rep. |
96 |
5,508.900 |
912.7 |
| Saudi Arabia |
91 |
6,564.300 |
1308.2 |
| Slovak Republic |
89 |
2,183.500 |
628.9 |
| Kenya |
86 |
854.900 |
1514.6 |
| Romania |
80 |
2,662.800 |
833.5 |
| Bulgaria |
80 |
1,389.900 |
513.7 |
| Peru |
78 |
2,632.400 |
155.5 |
| Slovenia |
76 |
2,283.800 |
961.9 |
| Ecuador |
65 |
1,547.100 |
261.7 |
| Uganda |
64 |
604.000 |
1754.4 |
| Serbia |
60 |
2,729.700 |
538.9 |
| United Arab Emirates |
60 |
994.700 |
1404.5 |
| Croatia |
59 |
2,683.600 |
437.4 |
| Ethiopia |
54 |
2,692.800 |
1576.0 |
| Ghana |
54 |
1,339.300 |
1375.4 |
| Iraq |
52 |
1,281.800 |
658.6 |
| Kazakhstan |
51 |
277.900 |
968.9 |
| Tanzania |
48 |
549.300 |
1648.7 |
| Algeria |
47 |
359.400 |
505.7 |
| Nepal |
46 |
1,056.800 |
739.2 |
| Belarus |
40 |
255.300 |
509.3 |
| Venezuela, RB |
39 |
724.400 |
240.7 |
| Sri Lanka |
38 |
858.500 |
1159.4 |
| Latvia |
38 |
441.900 |
508.6 |
| Cuba |
38 |
403.000 |
386.5 |
| Lithuania |
36 |
1,386.700 |
819.0 |
| Estonia |
33 |
985.800 |
893.3 |
| Tunisia |
33 |
720.500 |
1068.2 |
| Jordan |
32 |
992.700 |
1195.4 |
| Morocco |
28 |
1,070.800 |
1562.6 |
| Zimbabwe |
28 |
326.900 |
1082.5 |
| Uruguay |
27 |
1,006.500 |
413.3 |
| Cameroon |
27 |
490.300 |
1434.3 |
| Sudan |
27 |
254.000 |
1272.5 |
| Uzbekistan |
27 |
153.000 |
488.3 |
| Lebanon |
26 |
738.600 |
1449.2 |
| Cyprus |
26 |
582.400 |
1292.7 |
| Costa Rica |
25 |
1,879.400 |
106.2 |
| Luxembourg |
23 |
303.400 |
1740.8 |
| Armenia |
23 |
289.900 |
1349.2 |
| Georgia |
22 |
114.200 |
853.7 |
| Oman |
21 |
524.700 |
528.2 |
| Cambodia |
20 |
209.400 |
975.8 |
| Bolivia |
20 |
159.100 |
166.9 |
| Albania |
20 |
141.600 |
434.2 |
| Qatar |
19 |
761.700 |
1327.9 |
| Kuwait |
19 |
510.600 |
1467.0 |
| Bosnia and Herzegovina |
19 |
425.800 |
340.9 |
| Iceland |
19 |
329.000 |
1345.1 |
| Syrian Arab Republic |
19 |
197.800 |
831.1 |
| Azerbaijan |
19 |
142.700 |
620.6 |
| Myanmar |
19 |
63.300 |
522.0 |
| Congo, Dem. Rep. |
18 |
44.500 |
1426.9 |
| West Bank and Gaza |
17 |
276.200 |
1419.5 |
| Paraguay |
17 |
209.900 |
79.2 |
| Zambia |
16 |
166.000 |
1527.3 |
| Panama |
15 |
168.500 |
1354.7 |
| Malawi |
15 |
124.600 |
1749.7 |
| Dominican Republic |
15 |
57.100 |
439.5 |
| Mozambique |
14 |
592.300 |
257.5 |
| Yemen, Rep. |
14 |
122.200 |
1044.3 |
| Senegal |
14 |
98.700 |
942.6 |
| Mongolia |
14 |
82.400 |
554.6 |
| Moldova |
14 |
58.600 |
657.7 |
| Kyrgyz Republic |
14 |
31.400 |
1126.3 |
| Bahrain |
13 |
1,078.600 |
1610.1 |
| North Macedonia |
13 |
141.400 |
447.3 |
| Guatemala |
13 |
127.200 |
327.1 |
| Burkina Faso |
13 |
62.000 |
1704.4 |
| Rwanda |
12 |
65.000 |
1537.9 |
| Hong Kong SAR, China |
11 |
277.500 |
1767.6 |
| Botswana |
10 |
177.600 |
845.7 |
| Libya |
10 |
74.200 |
855.0 |
| Cote d'Ivoire |
10 |
53.300 |
888.9 |
| Afghanistan |
10 |
18.100 |
891.3 |
| El Salvador |
9 |
316.000 |
27.4 |
| Nicaragua |
9 |
118.700 |
117.4 |
| Tajikistan |
9 |
32.600 |
180.4 |
| Trinidad and Tobago |
8 |
23.600 |
892.8 |
| Somalia |
8 |
3.200 |
922.9 |
| Angola |
7 |
123.500 |
136.6 |
| Namibia |
7 |
68.100 |
1082.1 |
| Papua New Guinea |
7 |
28.900 |
1175.2 |
| Madagascar |
7 |
12.100 |
1135.1 |
| Malta |
6 |
178.100 |
1060.9 |
| Mauritius |
6 |
56.600 |
948.3 |
| Mali |
6 |
45.800 |
1861.3 |
| Niger |
6 |
21.500 |
1341.9 |
| Jamaica |
5 |
288.500 |
1294.6 |
| Benin |
5 |
267.300 |
1542.1 |
| Montenegro |
5 |
145.900 |
443.6 |
| Lao PDR |
5 |
144.400 |
1535.2 |
| Kosovo |
5 |
141.500 |
657.2 |
| Fiji |
5 |
51.100 |
1053.1 |
| Honduras |
5 |
39.600 |
552.8 |
| Gambia, The |
5 |
12.200 |
1330.9 |
| Brunei Darussalam |
4 |
80.100 |
866.3 |
| Togo |
4 |
31.300 |
1168.4 |
| Gabon |
4 |
25.700 |
2010.2 |
| Korea, Dem. People's Rep. |
4 |
16.000 |
522.6 |
| Curacao |
4 |
9.800 |
1305.8 |
| South Sudan |
4 |
2.900 |
1242.2 |
| Puerto Rico |
3 |
235.800 |
1745.7 |
| Bahamas, The |
3 |
70.700 |
68.2 |
| St. Kitts and Nevis |
3 |
58.200 |
1867.0 |
| French Polynesia |
3 |
20.300 |
1685.9 |
| Sierra Leone |
3 |
14.700 |
1733.4 |
| Bermuda |
3 |
14.500 |
1786.1 |
| Guinea-Bissau |
3 |
12.000 |
2115.5 |
| Greenland |
3 |
9.100 |
925.7 |
| Liechtenstein |
3 |
5.500 |
1038.0 |
| Monaco |
3 |
3.100 |
559.7 |
| Turkmenistan |
3 |
2.500 |
583.0 |
| Mauritania |
3 |
2.400 |
2160.2 |
| Maldives |
3 |
2.000 |
561.7 |
| Liberia |
3 |
1.900 |
1894.5 |
| Belize |
3 |
1.500 |
493.6 |
| Antigua and Barbuda |
2 |
238.800 |
782.5 |
| Bhutan |
2 |
16.500 |
284.9 |
| Eswatini |
2 |
15.900 |
1097.1 |
| Congo, Rep. |
2 |
13.600 |
1481.1 |
| Suriname |
2 |
10.300 |
1664.0 |
| Guyana |
2 |
7.900 |
841.2 |
| New Caledonia |
2 |
7.900 |
1129.7 |
| Burundi |
2 |
6.100 |
899.1 |
| Sint Maarten (Dutch part) |
2 |
3.300 |
716.5 |
| St. Lucia |
2 |
2.300 |
1605.0 |
| Chad |
2 |
2.300 |
1534.5 |
| Haiti |
2 |
0.800 |
1184.9 |
| Seychelles |
2 |
0.500 |
1607.5 |
| Sao Tome and Principe |
1 |
73.200 |
238.4 |
| Grenada |
1 |
46.400 |
1274.2 |
| Barbados |
1 |
41.100 |
1034.9 |
| Lesotho |
1 |
25.300 |
574.5 |
| British Virgin Islands |
1 |
15.100 |
1298.5 |
| Cabo Verde |
1 |
15.000 |
221.9 |
| Guinea |
1 |
10.800 |
1612.1 |
| Faroe Islands |
1 |
9.600 |
1128.0 |
| St. Vincent and the Grenadines |
1 |
4.200 |
1091.3 |
| Marshall Islands |
1 |
4.100 |
448.7 |
| Micronesia, Fed. Sts. |
1 |
1.000 |
1288.9 |
| Cayman Islands |
1 |
1.000 |
422.1 |
| Andorra |
1 |
0.800 |
1800.6 |
| Timor-Leste |
1 |
0.800 |
830.0 |
| Aruba |
1 |
0.700 |
1493.4 |
| Gibraltar |
1 |
0.600 |
1149.9 |
| Palau |
1 |
0.600 |
1770.1 |
| Eritrea |
1 |
0.500 |
258.3 |
| Central African Republic |
1 |
0.400 |
1483.0 |
| Isle of Man |
1 |
0.300 |
2235.4 |
# restrict data for plotting so we only plot countries with at least 5 universities
all_three_descriptives <- all_three_descriptives %>%
filter(n_universities >= 5)
gdp <- WDI::WDI(start = 2019, end = 2019)
# plot n papers against average apc
p <- all_three_descriptives %>%
left_join(wdi, by = c("country_code" = "iso2c", "country" = "country")) %>%
ggplot(aes(sum_fractional_works, mean_apc, colour = region, label = country)) +
geom_point() +
scale_x_log10(labels = scales::comma) +
scale_y_continuous(labels = scales::comma) +
theme(legend.position = "top") +
labs(y = NULL, colour = NULL, x = "Sum of fractional publications")
p

plotly::ggplotly(p)
pdata <- all_three_descriptives %>%
left_join(gdp, by = c("country_code" = "iso2c", "country" = "country")) %>%
left_join(wdi, by = c("country_code" = "iso2c", "country" = "country"))
labels <- pdata %>%
mutate(label = case_when(
country %in% c("China", "India", "United States","Uganda",
"Brazil", "Switzerland", "Israel", "Spain",
"Saudi Arabia") ~ country,
TRUE ~ ""))
p <- pdata %>%
ggplot(aes(NY.GDP.PCAP.KD, mean_apc, colour = region, label = country)) +
geom_point(aes(alpha = sum_fractional_works)) +
ggrepel::geom_text_repel(data = labels, aes(label = label),
show.legend = FALSE, max.overlaps = Inf,
box.padding = 1, min.segment.length = 0,
nudge_y = -10) +
scale_x_continuous(labels = scales::dollar) +
scale_y_continuous(labels = scales::comma) +
scale_alpha_continuous(trans = "log10", range = c(.1, 1),
labels = scales::comma) +
scale_colour_discrete_qualitative(palette = "Dark 3") +
theme(legend.position = "top", legend.box = "vertical") +
labs(y = "Average APC", colour = NULL, x = "GDP per capita",
alpha = "Number of fractional publications")
p

plotly::ggplotly(p)
spark_disconnect(sc)